CN117131992A - Big data electric power rush-repair hot spot prediction system - Google Patents
Big data electric power rush-repair hot spot prediction system Download PDFInfo
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Abstract
The application discloses a big data power rush-repair hot spot prediction system, which relates to the technical field of power maintenance and comprises a power monitoring module, a power analysis module, a fault information acquisition module, a data arrangement storage module, an environment information module, a cause and effect judgment module and a prediction model construction module; the power monitoring module is used for acquiring power consumption data of a target area; the power analysis module is used for analyzing the actual peak power utilization time period, the flat peak power utilization time period and the valley power utilization time period of the target area according to the power utilization data; according to the big data power rush-repair hot spot prediction system, the power monitoring module, the power analysis module, the fault information acquisition module, the data arrangement storage module, the environment information module and the cause and effect judgment module are arranged, so that the environmental factors which are relevant to the influence of the power fault prediction result on the big fault can be screened out, the prediction model is built by the purposefully selected environmental factors, and the predicted result is more accurate.
Description
Technical Field
The application relates to the technical field of power maintenance, in particular to a big data power rush-repair hot spot prediction system.
Background
Power operation refers to a series of operations that perform maintenance, overhaul, service, and management on electrical equipment. The power operation and maintenance is an important link for guaranteeing the safe and stable operation of the power system and is also one of important responsibilities of power enterprises.
The application discloses a Chinese patent with publication number of CN116008734B, which discloses a power information equipment fault prediction method based on data processing, and relates to the field of power data processing, current quantity is monitored in real time through a flow monitoring component, a loss function value is obtained through a constructed loss function model after current quantity loss parameters are obtained, fault prediction is started under a threshold condition, operation of fault occurrence rate is carried out through a fault prediction model, fault occurrence is directly positioned to the output condition of a specific power transmission line, when the fault occurs, corresponding exclusion screening is directly carried out through current quantity loss parameters and derivative change values of each power transmission line, the fault region can be visually displayed on which power transmission line is concentrated, the prediction of faults of a current power transmission system can be carried out through a prediction model, and the problems that whether the power transmission line is damaged or not and the fault region is concentrated on which section are determined through manual high-altitude section-by section investigation in the existing power transmission substation maintenance process are solved, and the method has low processing efficiency and can not predict the fault occurrence in advance are solved.
However, factors affecting power equipment faults and causing power repair are possibly various, and may be that the power load is too high, the equipment is overloaded to cause faults, the equipment is too high in temperature to cause faults, the humidity, the rainwater, the wind force are too high, and the like, and when fault analysis is performed, whether the factors causing faults have causality with the faults or not needs to be analyzed, and then the specific relation between the factors and the faults is analyzed.
Disclosure of Invention
The application aims to provide a big data power rush-repair hot spot prediction system which aims to solve the defects in the prior art.
In order to achieve the above object, the present application provides the following technical solutions: a big data power rush-repair hot spot prediction system comprises a power monitoring module, a power analysis module, a fault information acquisition module, a data arrangement storage module, an environment information module, a cause and effect judgment module and a prediction model construction module;
the power monitoring module is used for acquiring power consumption data of a target area;
the power analysis module is used for analyzing the actual peak power utilization time period, the flat peak power utilization time period and the valley power utilization time period of the target area according to the power utilization data;
the fault information acquisition module is used for acquiring power fault information of a target area;
the data arrangement storage module is used for dividing faults into corresponding power utilization time periods according to occurrence time and storing the data;
the environment information module is used for acquiring environment information of a target area and preprocessing the acquired environment information;
the causal judgment module is used for analyzing the relevance of each environmental information and fault occurrence, setting a judgment threshold value, obtaining the environmental information of which the relevance of the target area is greater than the judgment threshold value, and marking the environmental information as a fault factor;
the prediction model construction module is used for training to obtain a fault prediction model of each period of the target area by using fault factor information of each period of the target area, corresponding power consumption data and power fault information as samples.
Further, when the environment information module acquires the environment information, synchronously acquiring the time corresponding to the environment information, binding the time with the environment information, and storing the environment information bound with the corresponding time.
Further, the cause and effect judgment module obtains the fault factor through a first judgment and a second judgment, wherein the first judgment comprises the following steps:
a1, respectively acquiring fault information of a peak power utilization period, a flat peak power utilization period and a valley power utilization period;
a2, training a first fault prediction model obtained by using electricity consumption, environment information and fault times for each type of electricity consumption time period, wherein the environment information comprises a plurality of different environment factor information;
a3, predicting the failure times in T hours of an applied power period by using a first failure prediction model, and calculating the variance of the predicted failure times and the actual failure times to obtain a first variance, wherein T is a positive integer;
a4, sequentially screening out the electricity consumption and environmental factor information of each electricity consumption period, and respectively training to obtain a corresponding second fault prediction model;
a5, respectively predicting the failure times in the corresponding power-on time period and the T hours by using the obtained second failure prediction models, and calculating the variances of the predicted failure times and the actual failure times to obtain corresponding second variances respectively;
and a6, comparing the first variance with each second variance, if the first variance is smaller than the second variance, judging that the screened information obtained when the second prediction model corresponding to the second variance is obtained through training is the fault factor of the electricity utilization period, and recording the information as the first fault factor.
Further, the second judging includes the steps of:
b1, respectively acquiring fault information of a peak power utilization period, a flat peak power utilization period and a valley power utilization period, and classifying faults according to fault reasons;
b2, acquiring the occurrence time of each type of fault cause, and respectively acquiring the power consumption and environmental factor information corresponding to the time;
b3, for each type of fault reasons, respectively preparing a scatter diagram of electricity consumption and fault times, and environment factors and fault times;
and b4, drawing a fitting curve of each scatter diagram, calculating the slope of each tangent line of the fitting curve, setting a slope threshold value, judging the power consumption or environmental factor corresponding to the tangent slope with the absolute value larger than the slope threshold value if the absolute value of the tangent slope is larger than the slope threshold value, and marking the power consumption or environmental factor as a fault factor corresponding to the fault cause as a second fault factor.
Further, the fault prediction model constructed by the prediction model construction module comprises a first fault prediction model and a second fault prediction model;
the first fault prediction model is provided with a plurality of fault times for predicting each power utilization period respectively;
the second fault prediction model is provided with a plurality of times for predicting each fault cause of each electricity utilization period.
Further, the first fault prediction model is a neural network model, and is trained by using the first fault factor and the fault times as training samples.
Further, the second fault prediction model is a neural network model, and is trained by using the second fault factor and the fault times as training samples.
1. Compared with the prior art, the big data power rush-repair hot spot prediction system provided by the application has the advantages that the power monitoring module, the power analysis module, the fault information acquisition module, the data arrangement storage module, the environment information module and the cause and effect judgment module are arranged, the environmental factors which have great influence on the power fault prediction result and are related to the big fault can be screened out, the prediction model is constructed by purposefully selecting the environmental factors, and the predicted result is more accurate.
2. Compared with the prior art, the large data power rush-repair hot spot prediction system provided by the application has the advantages that the prediction model construction module is arranged, so that the total failure times of all power consumption periods and the failure cause times of the period can be predicted, the total prediction of the failures of all power consumption periods and the specific prediction of the failure cause times of all power consumption periods can be realized, the prediction is more careful, and the preparation of workers in advance is more convenient.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
Fig. 1 is a block diagram of a system structure according to an embodiment of the present application.
Detailed Description
In order to make the technical scheme of the present application better understood by those skilled in the art, the present application will be further described in detail with reference to the accompanying drawings.
Example embodiments will be described more fully hereinafter with reference to the accompanying drawings, but may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise. Furthermore, the terms "mounted," "connected," "coupled," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Embodiments of the disclosure and features of embodiments may be combined with each other without conflict.
As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
Embodiments described herein may be described with reference to plan and/or cross-sectional views with the aid of idealized schematic diagrams of the present disclosure. Accordingly, the example illustrations may be modified in accordance with manufacturing techniques and/or tolerances. Thus, the embodiments are not limited to the embodiments shown in the drawings, but include modifications of the configuration formed based on the manufacturing process. Thus, the regions illustrated in the figures have schematic properties and the shapes of the regions illustrated in the figures illustrate the particular shapes of the regions of the elements, but are not intended to be limiting.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Referring to fig. 1, a big data power rush-repair hot spot prediction system includes a power monitoring module, a power analysis module, a fault information acquisition module, a data arrangement storage module, an environment information module, a cause and effect judgment module, and a prediction model construction module;
the power monitoring module is used for acquiring power consumption data of a target area; and acquiring electricity consumption data of the target area through connection with an electric power network and electric power equipment of the target area.
The power analysis module is connected with the power monitoring module and is used for analyzing the actual peak power consumption time period, the flat peak power consumption time period and the low valley power consumption time period of the target area according to the power consumption data, and the actual peak power consumption time period, the flat peak power consumption time period and the low valley power consumption time period are different from the peak time period, the flat peak time period and the low valley time period which are set by a power company and are set according to the actual power consumption of the target area and industry standards.
The fault information acquisition module is used for acquiring power fault information of the target area, and the power fault information is acquired by manually inputting the fault information into the fault information acquisition module when the power fault occurs.
The data arrangement storage module is connected with the fault information acquisition module and the power analysis module and is used for dividing faults into corresponding power utilization time periods according to occurrence time, storing the data, preprocessing the data before storage, such as data cleaning, and screening out abnormal data, invalid data, redundant data and the like.
The environment information module is used for acquiring environment information of the target area and preprocessing the acquired environment information; when the environment information module acquires the environment information, synchronously acquiring the time corresponding to the environment information, binding the time with the environment information, and storing the environment information bound with the corresponding time.
The cause and effect judging module is connected with the environment information module and the data arrangement and storage module and is used for analyzing the relevance of each environment information and the occurrence of faults, setting a judging threshold value, obtaining the environment information of which the relevance of a target area is larger than the judging threshold value and marking the environment information as a fault factor; the cause and effect judgment module obtains a fault factor through first judgment and second judgment, wherein the first judgment comprises the following steps:
a1, respectively acquiring fault information of a peak power utilization period, a flat peak power utilization period and a valley power utilization period, wherein the fault information comprises fault reasons, fault time and fault places, and the fault reasons comprise various types, such as: short circuit fault, overload fault, open circuit fault, ground fault, harmonic fault, etc.;
and a2, for each type of electricity consumption period, training to obtain a first fault prediction model by using electricity consumption, environment information and fault times, wherein the environment information comprises various different environment factor information, temperature, humidity, wind power, rainfall, snow quantity and the like, and can be obtained by logging in a meteorological website.
a3, predicting the failure times in T hours of an applied power period by using a first failure prediction model, and calculating the variance of the predicted failure times and the actual failure times to obtain a first variance, wherein T is a positive integer;
a4, sequentially screening out the electricity consumption and environmental factor information of each electricity consumption period, and respectively training to obtain a corresponding second fault prediction model;
a5, respectively predicting the failure times in the corresponding power-on time period and the T hours by using the obtained second failure prediction models, and calculating the variances of the predicted failure times and the actual failure times to obtain corresponding second variances respectively;
the formula for calculating the variance of a3 and a5 is:
σ 2 (y t+1 I t )＜σ 2( y t+1 I t -X t )
wherein sigma 2 () Representing a variance function, t representing the current period, t+1 representing the predicted period, y t+1 Indicating the number of predicted faults, I t Representing all information sets participating in model training, X t Representing the screened information, sigma 2 (y t+1 I t ) Representing the variance, σ, of the number of predicted faults and the number of actual faults 2 (y t+1 )I t -X t ) Indicating the variance of the predicted number of faults and the actual number of faults after removing certain screened information.
and a6, comparing the first variance with each second variance, if the first variance is smaller than the second variance, judging that the screened information obtained when the second prediction model corresponding to the second variance is obtained through training is the fault factor of the electricity utilization period, and recording the information as the first fault factor.
The second judgment includes the steps of:
b1, respectively acquiring fault information of a peak power utilization period, a flat peak power utilization period and a valley power utilization period, and classifying faults according to fault reasons;
b2, acquiring the occurrence time of each type of fault cause, and respectively acquiring the power consumption and environmental factor information corresponding to the time;
b3, for each type of fault reasons, respectively preparing a scatter diagram of electricity consumption and fault times, and environment factors and fault times;
and b4, drawing a fitting curve of each scatter diagram, calculating the slope of each tangent line of the fitting curve, setting a slope threshold value, wherein the absolute value of the slope of the tangent line is larger than the slope threshold value, and the optimal slope threshold value is 0.3-0.5, judging the power consumption or environmental factor corresponding to the slope of the tangent line with the absolute value larger than the slope threshold value, and marking the power consumption or environmental factor as a fault factor corresponding to a fault reason as a second fault factor.
The prediction model construction module is connected with the cause and effect judgment module, the environment information module and the data arrangement storage module and is used for training to obtain a fault prediction model of each period of the target area by using fault factor information of each period of the target area, corresponding power consumption data and power fault information as samples.
The fault prediction model constructed by the prediction model construction module comprises a first fault prediction model and a second fault prediction model;
the first fault prediction model is provided with a plurality of fault times for predicting each power utilization period respectively; and the first fault prediction model selects a neural network model and uses a first fault factor and the number of faults as training samples for training. The total fault times of the peak electricity consumption period can be predicted by using a first fault prediction model trained by samples of the peak electricity consumption period/the flat electricity consumption period/the valley electricity consumption period; the total fault times of the flat peak electricity consumption period can be predicted by using a first fault prediction model trained by samples of the flat peak electricity consumption period; the total number of faults in the off-peak electricity usage period may be predicted by using a first fault prediction model trained using samples of the off-peak electricity usage period.
The second failure prediction model has a plurality of times for predicting each failure cause of each electricity consumption period. And the second fault prediction model is a neural network model, and is trained by using the second fault factors and the fault times as training samples. For example, for a peak electricity usage period, a second failure prediction model corresponding to the condition of an overload failure may predict the number of overload failures for the peak electricity usage period.
While certain exemplary embodiments of the present application have been described above by way of illustration only, it will be apparent to those of ordinary skill in the art that modifications may be made to the described embodiments in various different ways without departing from the spirit and scope of the application. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive of the scope of the application, which is defined by the appended claims.
Claims (7)
1. The utility model provides a big data electric power rush-repair hot spot prediction system which characterized in that: the system comprises a power monitoring module, a power analysis module, a fault information acquisition module, a data arrangement storage module, an environment information module, a cause and effect judgment module and a prediction model construction module;
the power monitoring module is used for acquiring power consumption data of a target area;
the power analysis module is used for analyzing the actual peak power utilization time period, the flat peak power utilization time period and the valley power utilization time period of the target area according to the power utilization data;
the fault information acquisition module is used for acquiring power fault information of a target area;
the data arrangement storage module is used for dividing faults into corresponding power utilization time periods according to occurrence time and storing the data;
the environment information module is used for acquiring environment information of a target area and preprocessing the acquired environment information;
the causal judgment module is used for analyzing the relevance of each environmental information and fault occurrence, setting a judgment threshold value, obtaining the environmental information of which the relevance of the target area is greater than the judgment threshold value, and marking the environmental information as a fault factor;
the prediction model construction module is used for training to obtain a fault prediction model of each period of the target area by using fault factor information of each period of the target area, corresponding power consumption data and power fault information as samples.
2. The big data power rush-repair hot spot prediction system according to claim 1, wherein: when the environment information module acquires the environment information, synchronously acquiring the time corresponding to the environment information, binding the time with the environment information, and storing the environment information bound with the corresponding time.
3. The big data power rush-repair hot spot prediction system according to claim 1, wherein: the cause and effect judgment module obtains a fault factor through first judgment and second judgment, wherein the first judgment comprises the following steps:
a1, respectively acquiring fault information of a peak power utilization period, a flat peak power utilization period and a valley power utilization period;
a2, training a first fault prediction model obtained by using electricity consumption, environment information and fault times for each type of electricity consumption time period, wherein the environment information comprises a plurality of different environment factor information;
a3, predicting the failure times in T hours of an applied power period by using a first failure prediction model, and calculating the variance of the predicted failure times and the actual failure times to obtain a first variance, wherein T is a positive integer;
a4, sequentially screening out the electricity consumption and environmental factor information of each electricity consumption period, and respectively training to obtain a corresponding second fault prediction model;
a5, respectively predicting the failure times in the corresponding power-on time period and the T hours by using the obtained second failure prediction models, and calculating the variances of the predicted failure times and the actual failure times to obtain corresponding second variances respectively;
and a6, comparing the first variance with each second variance, if the first variance is smaller than the second variance, judging that the screened information obtained when the second prediction model corresponding to the second variance is obtained through training is the fault factor of the electricity utilization period, and recording the information as the first fault factor.
4. The big data power rush-repair hot spot prediction system according to claim 3, wherein: the second judgment includes the steps of:
b1, respectively acquiring fault information of a peak power utilization period, a flat peak power utilization period and a valley power utilization period, and classifying faults according to fault reasons;
b2, acquiring the occurrence time of each type of fault cause, and respectively acquiring the power consumption and environmental factor information corresponding to the time;
b3, for each type of fault reasons, respectively preparing a scatter diagram of electricity consumption and fault times, and environment factors and fault times;
and b4, drawing a fitting curve of each scatter diagram, calculating the slope of each tangent line of the fitting curve, setting a slope threshold value, judging the power consumption or environmental factor corresponding to the tangent slope with the absolute value larger than the slope threshold value if the absolute value of the tangent slope is larger than the slope threshold value, and marking the power consumption or environmental factor as a fault factor corresponding to the fault cause as a second fault factor.
5. The big data power rush-repair hot spot prediction system according to claim 4, wherein: the fault prediction model constructed by the prediction model construction module comprises a first fault prediction model and a second fault prediction model;
the first fault prediction model is provided with a plurality of fault times for predicting each power utilization period respectively;
the second fault prediction model is provided with a plurality of times for predicting each fault cause of each electricity utilization period.
6. The big data power rush-repair hot spot prediction system according to claim 5, wherein: the first fault prediction model is a neural network model, and is trained by using a first fault factor and the number of faults as training samples.
7. The big data power rush-repair hot spot prediction system according to claim 5, wherein: and the second fault prediction model is a neural network model, and is trained by using a second fault factor and the number of faults as training samples.
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| CN104820874A (en) * | 2015-05-18 | 2015-08-05 | 国家电网公司 | Monitoring method for power dispatching center |
| US20200371858A1 (en) * | 2018-01-19 | 2020-11-26 | Hitachi, Ltd. | Fault Predicting System and Fault Prediction Method |
| CN114118219A (en) * | 2021-11-01 | 2022-03-01 | 北京宇航系统工程研究所 | Data-driven real-time abnormal detection method for health state of long-term power-on equipment |
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